Local positioning systems have historically been calibrated either through the application of active markers or by way of user input. Both procedures have a tendency to be a painstaking and time consuming process. Further, manual user inputs are subject to human error. Thus, there exists an opportunity to improve and provide a more efficient way of calibrating a local positioning system.